Techniques and Approaches of Facial Recognition under Occlusion: A Review

A human face is one of the most prominent features used in the process of authenticating technical applications in the domains of security, biometrics, surveillance and forensics. Recognition and detection of facial features has thus become challenging due to problems of occlusion, emotion, image resolution, varying facial expressions and aging. Such attributes tend to have a great impact on the overall performance of a robust facial recognition system. Hence, facial recognition with presence of occlusion triggers to be a hindrance in the natural environment and thereby limits the system model to recognise faces. For this purpose, multiple research authors have inhibited strategies and techniques to address the issues of occlusion. Numerous developments in the field of machine learning and deep learning have constantly evolved with complex architectures that could design the model from scratch and perform image processing to attain maximum efficiency. Such approaches have the potential to accomplish highest state-of-the art accuracy by minimizing error loss. Nevertheless, facial recognition that tends to bypass occlusion is still imperative to limitations for real?world applications. Hence in this review paper, the authors highlight various problems that a facial recognition system with occlusion might face and thereby proposes to analyse various methods of recognition in order to cope with the existing problems. The paper also focuses on extraction approaches thus used present the novelty. The review finally ends, with a mention of future challenges with regards to occluded facial recognition

Face Analysis Using Row and Correlation Based Local Directional Pattern

Face analysis, which includes face recognition and facial expression recognition, has been attempted by many researchers and gave ideal solutions. The problem is still active and challenging due to an increase in the complexity of the problem viz. due to poor lighting, face occlusion, low-resolution images, etc. Local pattern descriptor methods introduced to overcome these critical issues and improve the recognition rate. These methods extract the discriminant information from the local features of the face image for recognition. In this paper, the local descriptor based two methods, namely row-based local directional pattern and correlation-based local directional pattern proposed by extending an existing descriptor -- local directional pattern (LDP). Further, the two feature vectors obtained by these methods concatenated to form a hybrid descriptor. Experimentation has carried out on benchmark databases and results infer that the proposed hybrid descriptor outperforms the other descriptors in face analysis

Clasificación de accesorios a partir de información de profundidad

El objetivo de este trabajo de fin de grado (TFG) es la identificación robusta de complementos a partir de imágenes de profundidad (2.5D). Dichas imágenes serán adquiridas de una cámara Kinect II ubicada en una posición cenital. Los complementos evaluados en este caso son gorras y distintos tipos de sombreros (grandes, pequeños y medianos), que la solución propuesta debe ser capaz de identificar. La solución propuesta extrae un conjunto de descriptores por cada persona previamente detectada en la escena que, posteriormente, son clasificados utilizando la técnica PCA (Análisis de Componentes Principales), comparándolos con las distintas clases previamente entrenadas. El sistema desarrollado se ha evaluado realizando diferentes pruebas experimentales sobre secuencias de profundidad reales, obteniendo resultados satisfactorios. En concreto, se han obtenido tasas de acierto del 98% para el caso más sencillo (clasificación binaria) y superiores al 85% en los casos más complejos (cuatro o cinco clases).The aim of this final degree thesis is the robust identification of headgear accesories from depth images (2.5D) acquired using a Kinect II camera located in a zenithal position. The accesories evaluated in this work are caps and different types of hats (large, small and medium). The proposed solution must be able to identify complements of each class. The proposed solution extracts a set of descriptors for each person previously detected in the scene, which are then classified using the PCA (Principal Component Analysis) technique, comparing them with the different classes previously trained. The developed system has been evaluated by carrying out different experimental tests on real depth sequences, obtaining satisfactory results. Specifically, success rates of 98% have been obtained for the simplest case (binary classification) and higher than 85% in the most complex cases (four or five classes).Grado en Ingeniería Electrónica de Comunicacione